Organization

Time & Location

Work on Case Studies (Exercises): Thursdays, 11:00am - 12:00am in the same room, and group work

Overview

Upon completion of the course, participants will be able to use computational models for extracting diagnostic information from different types of clinical image data sets. These data sets may provide information, for example, about blood perfusion or micro-structural tissue properties, about metabolic processes, or patterns of disease progression. The participants will understand the physiological concepts underlying the computational algorithms employed, and will know of advantages and shortcomings of different modeling strategies. This will allow them to analyze clinical imaging protocols with respect to the underlying physiological information, and to propose diagnostic algorithms that combine anatomical and physiological information of different imaging. A focus will be on applications from neuroimaging.

During the first part of the course, participants will implement and evaluate small case studies in team of up to three people. The use of public Python libraries, such as NiPy and NiPype, is encouraged. Towards the end of the course, teams will demonstrate their work to other course participants in a short oral presentation and in a short summary paper.

NB:
There scripts for the case studies will work with a little effort on Ubuntu and Mac. For windows users I will recommend you run an ubuntu virtual machine for convenience.
You can check on this link for step by step tutorial on installing the Ubuntu virtual machine. Please install Ubuntu 16.04 LTS desktop version